November 20, 2014

Mapping croplands using Landsat data with generalized classifier over large

areas

Aparna Phalke and Prof. Mutlu Ozdogan

Nelson Institute for Environmental Studies University of Wisconsin - Madison

Updates on following

LDA model training and tuning

LDA results of sample footprints

Basic Definitions• Model tuning is the process in which one or more parameters of a device or model are adjusted upwards or downwards to achieve improved or specified results

• The aim of LDA model tuning is to calibrate the parameters of propagation models and improve the key performance indicators.

MethodologyR algorithm

• Divide training data in 75%-25% splits• LDA model trained on 75% data and tested on 25% data

• This procedure repeated 1000 times with random sets of train and test

• LDA model accuracy check with train and test within scene or within footprint.

ResultsGroup 1:

ResultsGroup 2:

ResultsGroup 3:

ResultsGroup 4:

ResultsGroup 5:

ResultsGroup 6:

ResultsLDA model accuracy at different

levels

LDA classified image sample result: Mea

nStd

Min Variance

Range

Counts

Slope Elevation

MaxInputs

LDA classified image results

turkey Group/zone Allmean 1.11E-03 6.10E-04 5.97E-04

sd -1.83E-03 -1.58E-03 -1.62E-03max -8.13E-05 -1.21E-04 -1.23E-04min -3.44E-05 -3.46E-04 -2.09E-04var 1.34E-07 1.82E-07 1.37E-07

range -7.63E-05 -5.60E-05 1.13E-05count 1.99E-02 -1.67E-02 1.02E-02slope 7.91E-02 9.21E-02 1.39E-01

elevation 2.82E-03 1.03E-03 1.88E-04

LDA coefficients of sample example

Own Within

group/zone All

LDA classified image sample results

Conclusion Model tuning helped us in understanding dynamics of whole process, which gives a more accurate picture of how the model is behaving.

Thank youphalke@wisc.edu